Embodiments of the present invention relate to analyzing sequential data, and more specifically to hierarchically clustering sequential data.
Sequential data, i.e., a dataset including sequential information, can represent a variety of different types of data. For example, such a dataset can include records of product purchases after other purchases, records of web page requests after other page requests, records of regions of a document or application viewed after other regions are viewed, etc. The sequence can represent a path, i.e., a sequence of two or more positions connected in a particular order. Clustering of such sequential data can be useful in analysis of such data to, for example, help identify and/or understand higher-level patterns.
Analysis of paths is performed in various different fields or domains. For example, in eye tracking analysis, scanpaths representing users' eye movements while viewing a scene may be analyzed to determine high-level scanning strategies. The scanning strategies determined from such an analysis may be used to improve product designs. For example, by studying scanpaths for users viewing a web page, common viewing trends may be determined and used to improve the web page layout. Various other types of analyses on paths may be performed in other fields. Accordingly, new and improved techniques are always desirable for analyzing sequential information that can provide insight into characteristics of the sequences that facilitate comparisons of sequences of data.